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Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability.

Objective: to build, validate, and evaluate an educational health website on home care for newborns for use by pregnant women, postpartum women, and family members. Method: methodological study developed according to the Analyze, Design, Develop, Implement, and Evaluate model. After construction, the website was validated by 20 experts and evaluated by 20 individuals from the target audience, and the data wasanalyzed according to the Concordance Index with a cut-off point equal to or greater than 0.7 (70%). Results: in the validation, the Concordance Index for all the items was higher than 0.7 (70%), with a variation between 0.75 (75%) and 1 (100%), reaching an overall average value of 0.91 (91%). In the evaluation, all the items got top marks, with anoverall average value of 1 (100%). Conclusion: the educational website was built, validated, and evaluated in a satisfactory manner. It can be considered an appropriate tool for its purpose, with benefits in the teaching-learning process for families regarding postnatal home care fornewborns through its use. It can also be used to educate students and health professionals. The website is available for free access via laptops, computers, smartphones, or tablets.

ObjectiveTo utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.Materials and methodsWe developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data.ResultsOur public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems.Discussion and conclusionsWe present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

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ObjectivesTo demonstrate the application of the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) principles described in our companion article to hypertension treatments and assess internal and external validity of the generated evidence.Materials and methodsLEGEND defines a process for high-quality observational research based on 10 guiding principles. We demonstrate how this process, here implemented through large-scale propensity score modeling, negative and positive control questions, empirical calibration, and full transparency, can be applied to compare antihypertensive drug therapies. We assess internal validity through covariate balance, confidence-interval coverage, between-database heterogeneity, and transitivity of results. We assess external validity through comparison to direct meta-analyses of randomized controlled trials (RCTs).ResultsFrom 21.6 million unique antihypertensive new users, we generate 6 076 775 effect size estimates for 699 872 research questions on 12 946 treatment comparisons. Through propensity score matching, we achieve balance on all baseline patient characteristics for 75% of estimates, observe 95.7% coverage in our effect-estimate 95% confidence intervals, find high between-database consistency, and achieve transitivity in 84.8% of triplet hypotheses. Compared with meta-analyses of RCTs, our results are consistent with 28 of 30 comparisons while providing narrower confidence intervals.ConclusionWe find that these LEGEND results show high internal validity and are congruent with meta-analyses of RCTs. For these reasons we believe that evidence generated by LEGEND is of high quality and can inform medical decision-making where evidence is currently lacking. Subsequent publications will explore the clinical interpretations of this evidence.

Evidence derived from existing health-care data, such as administrative claims and electronic health records, can fill evidence gaps in medicine. However, many claim such data cannot be used to estimate causal treatment effects because of the potential for observational study bias; for example, due to residual confounding. Other concerns include P hacking and publication bias. In response, the Observational Health Data Sciences and Informatics international collaborative launched the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) research initiative. Its mission is to generate evidence on the effects of medical interventions using observational health-care databases while addressing the aforementioned concerns by following a recently proposed paradigm. We define 10 principles of LEGEND that enshrine this new paradigm, prescribing the generation and dissemination of evidence on many research questions at once; for example, comparing all treatments for a disease for many outcomes, thus preventing publication bias. These questions are answered using a prespecified and systematic approach, avoiding P hacking. Best-practice statistical methods address measured confounding, and control questions (research questions where the answer is known) quantify potential residual bias. Finally, the evidence is generated in a network of databases to assess consistency by sharing open-source analytics code to enhance transparency and reproducibility, but without sharing patient-level information. Here we detail the LEGEND principles and provide a generic overview of a LEGEND study. Our companion paper highlights an example study on the effects of hypertension treatments, and evaluates the internal and external validity of the evidence we generate.

ObjectivePredicting patient outcomes using healthcare/genomics data is an increasingly popular/important area. However, some diseases are rare and require data from multiple institutions to construct generalizable models. To address institutional data protection policies, many distributed methods keep the data locally but rely on a central server for coordination, which introduces risks such as a single point of failure. We focus on providing an alternative based on a decentralized approach. We introduce the idea using blockchain technology for this purpose, with a brief description of its own potential advantages/disadvantages.Materials and methodsWe explain how our proposed EXpectation Propagation LOgistic REgRession on Permissioned blockCHAIN (ExplorerChain) can achieve the same results when compared to a distributed model that uses a central server on 3 healthcare/genomic datasets, and what trade-offs need to be considered when using centralized/decentralized methods. We explain how the use of blockchain technology can help decrease some of the problems encountered in decentralized methods.ResultsWe showed that the discrimination power of ExplorerChain can be statistically similar to its counterpart central server-based algorithm. While ExplorerChain inherited some benefits of blockchain, it had a small increased running time.DiscussionExplorerChain has the same prerequisites as a distributed model with a centralized server for coordination. In a manner similar to secure multi-party computation strategies, it assumes that participating institutions are honest, but "curious."ConclusionWhen evaluated on relatively small datasets, results suggest that ExplorerChain, which combines artificial intelligence and blockchain technologies, performs as well as a central server-based method, and may avoid some risks at the cost of efficiency.

ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.

Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, blockchain represents a data architecture, whose application goes far beyond Bitcoin - the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data. However, critical in assessing whether blockchain can fulfill the hype of a technology characterized as 'revolutionary' and 'disruptive', is the need to ensure that blockchain design elements consider actual healthcare needs from the diverse perspectives of consumers, patients, providers, and regulators. In addition, answering the real needs of healthcare stakeholders, blockchain approaches must also be responsive to the unique challenges faced in healthcare compared to other sectors of the economy. In this sense, ensuring that a health blockchain is 'fit-for-purpose' is pivotal. This concept forms the basis for this article, where we share views from a multidisciplinary group of practitioners at the forefront of blockchain conceptualization, development, and deployment.

Purpose: This study aimed to evaluate the preliminary effectiveness of an educational intervention using a web-app to improve knowledge of breast cancer risk factors and symptoms and adherence to healthy eating and physical activity among women without breast cancer diagnosis in Asturias (Spain). Methods: A pragmatic randomized pilot trial was conducted to evaluate the impact of a web-app-based intervention for women without breast cancer diagnosis. Women in the intervention group participated in a 6-month intervention web-app based on the Behaviour Change Wheel Model. The web-app includes information about breast cancer risk factors, early detection, physical activity and diet. Results: Two hundred and eighty-fifth women aged 25–50 were invited to join the study. Two hundred and twenty-four were randomly assigned to either the intervention group (IG = 134) or control group (CG = 90) according to their place of residence. Adherence among women in the IG increased significantly from pre- to post-intervention for eight of the 12 healthy behaviors and for the identification of six risk factors and six symptoms compared to women in the CG and, among whom adherence only increased for two behaviors, the identification of one risk factor and 0 symptoms. The intervention significantly improved the mean number of risk factors + 1.06 (p < 0.001) and symptoms + 1.18 (p < 0.001) identified by women in the IG. Conclusions: The preliminary results of this study suggest that an educational intervention using a web-app and based on the Behaviour Change Wheel model could be useful to improve knowledge of breast cancer risk factors and symptoms and to improve adherence to a healthy diet and physical activity in women without a previous breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

ObjectiveDecentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the "server" role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness, mitigate the risks of a centralized architecture, and compute the models in a fair way.Materials and methodsWe propose a framework that includes both server and "client" roles to preserve correctness. We adopt a blockchain network to obtain the benefits of decentralization, by alternating the roles for each site to ensure computational fairness. Also, we developed GloreChain (Grid Binary LOgistic REgression on Permissioned BlockChain) as a concrete example, and compared it to a centralized algorithm on 3 healthcare or genomic datasets to evaluate predictive correctness, number of learning iterations and execution time.ResultsGloreChain performs exactly the same as the centralized method in terms of correctness and number of iterations. It inherits the advantages of blockchain, at the cost of increased time to reach a consensus model.DiscussionOur framework is general or flexible and can also address intrinsic challenges of blockchain networks. Further investigations will focus on higher-dimensional datasets, additional use cases, privacy-preserving quality concerns, and ethical, legal, and social implications.ConclusionsOur framework provides a promising potential for institutions to learn a predictive model based on healthcare or genomic data in a privacy-preserving and decentralized way.

ABSTRACT Objectives: to develop and analyze content validity evidence of a website for patients with coronary artery disease. Methods: a methodological study, carried out in the phases: Definition - determined contents for inclusion in the website, architecture and design; Implementation - subjects included in the website; Assessment - website submitted to analysis by 13 experts and eight laypersons regarding organization, content and design, on a scale of 1 (no agreement) to 4 (complete agreement). Items that reached Content Validity Ratio (CVR) higher than the established critical values and Content Validity Index greater than 0.80 were considered valid. Results: eight domains related to secondary prevention in coronary heart disease were included on the website. Critical CVR and adequate CVI were obtained according to professional and lay experts. Conclusions: the website was developed, achieving adequate content validity evidence, and can be used as an educational tool for this population.

ObjectiveWe describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations.Materials and methodsHigh-end networking, packet-filter firewalls, network intrusion-detection systems.ResultsWe describe a "Medical Science DMZ" concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs.DiscussionThe exponentially increasing amounts of "omics" data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research "Big Data." The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows.ConclusionBy implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.

ObjectiveTo assess the feasibility of a novel, partnered technology development process to co-create mobile health applications (apps) addressing community health priorities, using psychoeducation of cognitive behavioral therapy (CBT) principles for enhancing resilience as an example.DesignStakeholder engagement, workgroups, pilot feasibility study using mixed methods during October 2013 through January 2016 over three phases: 1) defining the vision of the project and increasing technical capacity, 2) co-development and pilot testing of the app, and 3) planning for sustainability.SettingAn academic-community partnership in South Los Angeles, California.ParticipantsEight stakeholders; 30 pilot participants from the community.Main outcome measuresQualitative analysis of audio-recordings of the app development process and stakeholder interviews, surveys of stakeholders' perception of the development process, app use data, and feedback from pilot participants.ResultsThe participatory technology development process resulted in creation and pilot-testing of a resiliency-focused text messaging app. Of the 1,107 messages sent, 23 out of 30 (77%) app users responded to explore interactive content. Stakeholders reported increased perceived competency in creating mobile apps and that the process fostered a culture of co-leadership. There was also sustained engagement in mobile app development by stakeholders beyond the initial project period.ConclusionsThis is the first study, to our knowledge, to demonstrate the feasibility of participatory technology development, an approach involving direct participation in the development, tailoring and maintenance of a mobile app by a broad set of stakeholders with high representation from racial/ethnic minorities from an under-resourced community. Participatory technology development is a promising approach for creating sustainable, relevant and engaging health technologies across different technological, clinical and community settings.

Modeling of background radiation for the urban environment plays an important role in homeland security. However, background radiation is difficult to assess due to its spatial-temporal fluctuations caused by the variation in soil composition, building materials, and weather patterns etc. To address the challenge of background radiation modeling, we developed a mobile sensor network to continuously monitor the background radiation; we also proposed a maximum likelihood estimation algorithm to decouple and estimate the backgrounds spatial distribution and temporal fluctuation. Experimental results demonstrated how this background radiation monitoring system accurately recognized high background regions in the experimental area, and successfully captured temporal fluctuation trends of background radiation during rains. Our system provides an efficient solution to model the temporal fluctuation and spatial distribution of background radiation.

Dedicated computing resources are expensive to develop, maintain, and administrate. Frequently, research groups require bursts of computing power, during which progress is still limited by available computing resources. One way to alleviate this bottleneck would be to use additional computing resources. Today, many computing devices remain idle most of the time. Passive volunteer computing exploits this unemployed reserve of computing power by allowing device-owners to donate computing time on their own devices. Another complementary way to alleviate bottlenecks in computing resources is to use more efficient algorithms. Engaging volunteer computing employs human intuition to help solve challenging problems for which efficient algorithms are difficult to develop or unavailable. Designing engaging volunteer computing projects is challenging but can result in high-quality solutions. Here, we highlight four examples.

We first propose a novel protocol to realize quantum cloning of an arbitrary unknown shared state with assistance offered by a state preparer. The initial phase of this protocol involves the utilization of quantum teleportation (QT), enabling the transfer of quantum information from an arbitrary number of senders to another arbitrary number of receivers through a maximally entangled GHZ-type state serving as a network channel, without centralizing the information at any specific location. In the second stage of this protocol, the state preparer performs a special single-qubit projective measurement and multiple Z-basis measurements and then communicates a number of classical bits corresponding to measurement results, the perfect copy or orthogonal-complementing copy of an unknown shared state can be produced at senders hands. Then, using a non-maximally entangled GHZ-type state instead of the aforementioned quantum channel, we extend the proposed protocol from three perspectives: projective measurement, positive operator-value measurement (POVM), and a single generalized Bell-state measurement. Our schemes can relay quantum information over a network without requiring fully trusted central or intermediate nodes, and none of participants can fully access the information., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This article addresses critical health data integrity by proposing an HF (Hyperledger Fabric)-based architecture with integration into the global health data architecture based on distributed content-addressable storage networks.

Proposed is a Satellite network cache placement strategy (PNCCP) based on popularity and node cooperation to address the issue of significant delays in end-to-end connectivity due to instability among satellites. Initially, the strategy employs spectral clustering algorithm to partition the satellite network's topology, limiting the retrieval scope of content and reducing unnecessary propagation delays. Within each partition, a cache collaboration open mechanism among satellites is devised to share cache resources, utilizing the proximity of neighboring nodes to share popular content and cache space. Furthermore, the data naming network (NDN) cache model is enhanced and integrated with the open mechanism, with an update mechanism designed to address the invalidation caused by the dynamic nature of satellite networks. Finally, aiming to minimize users' average retrieval delay, the artificial bee colony algorithm is employed to solve the optimal cache placement problem. Simulation results demonstrate that compared to three contrasting cache strategies, the proposed strategy reduces user content retrieval delays, improves cache hit rates, and holds an advantage in reducing request hop counts., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Cognitive radio networks (CRN) enable wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables to satisfy Quality of Service (QoS) needs by reducing energy utilization. In CRN, spectrum sensing is an essential process that is highly challenging and can be addressed by several traditional techniques, such as energy detection, match filtering, etc. For now, the current models' performance is impacted by the comparatively low Signal to Noise Ratio (SNR) of recognized signals and the insignificant quantity of traditional signal samples. This research proposals a new spectral sensing technique for cognitive radio networks (SST-CRN) that addresses the drawbacks of predictable energy detection models. With the use of a deep belief network (DBN), the suggested model contributes to accomplish a nonlinear threshold based on the chicken swarm algorithm (CSA). The proposed DBN enabled SST-CRN technique goes through two phases in a organized process: offline and online. Throughout the offline phase, the DBN model is methodically trained on pre-gathered data, developing the aptitude to identify problematic patterns and examples from the spectral features of the radio environment. This stage involves extensive feature extraction, validation, and model development to ensure that the DBN can professionally represent complicated spectral dynamics. Additionally, online spectrum sensing is conducted during the real communication phase to enable real-time adaptation to dynamic changes in the spectrum environment. Offline spectrum sensing is typically performed during a devoted sensing period before actual communication begins. When combined with DBN's deep learning capabilities and CSO's innate nature-inspired algorithms, a synergistic framework is created that enables CRNs to explore and allocate incidences on their own with astonishing accuracy. The proposed solution considerably improves the spectrum efficiency and resilience of CRNs by harnessing the power of DBN, which leads to more effective resource utilization and less interference. The Simulation results show that our proposed strategy produces more accurate spectrum occupancy assessments. The result parameters such as probability of detection, SNR of -24dB, the SST-CRN perfect has increased a developed Pd of 0.810, whereas the existing methods RMLSSCRN-100 and RMLSSCRN-300 have accomplished a lower Pd of 0.577 and 0.736, respectively. Our deep learning methodology uses convolutional neural networks to automatically learn and adapt to dynamic and complicated radio environments, improving accuracy and flexibility over classic spectrum sensing approaches. Future research might focus on improving CSO algorithms to better optimize the spectrum sensing process, enhancing the reliability of DBN-enabled sensing techniques., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 M., E.. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

To achieve secure, reliable, and scalable traffic delivery, request streams in mobile Internet of Things (IoT) networks supporting Multi-access Edge Computing (MEC) typically need to pass through a service function chain (SFC) consisting of an ordered series of Virtual Network Functions (VNFs), and then arrive at the target application in the MEC for processing. The high mobility of users and the real-time variability of network traffic in IoT-MEC networks lead to constant changes in the network state, which results in a mismatch between the performance requirements of the currently deployed SFCs and the allocated resources. Meanwhile, there are usually multiple instances of the same VNF in the network, and proactively reconfiguring the deployed SFCs based on the network state changes to ensure high quality of service in the network is a great challenge. In this paper, we study the SFC Reconfiguration Strategy (SFC-RS) based on user mobility and resource demand prediction in IoT MEC networks, aiming to minimize the end-to-end delay and reconfiguration cost of SFCs. First, we model SFC-RS as Integer Linear Programming (ILP). Then, a user trajectory prediction model based on codec movement with attention mechanism and a VNF resource demand prediction model based on the Long Short-Term Memory (LSTM) network are designed to accurately predict user trajectories and node computational and storage resources, respectively. Based on the prediction results, a Prediction-based SFV Active Reconfiguration (PSAR) algorithm is proposed to achieve seamless SFC migration and routing update before the user experience quality degrades, ensuring network consistency and high quality service. Simulation results show that PSAR provides 51.28%, 28.60%, 21.75%, and 16.80% performance improvement over the existing TSRFCM, DDQ, OSA, and DPSM algorithms in terms of end-to-end delay reduction, and 33.32%, 18.94%, 67.42%, and 60.61% performance optimization in terms of reconfiguration cost reduction., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its value in healthcare continues to grow, as it can overlay on top of challenges dealing with the rising burden of chronic disease management and an aging population. To address difficulties associated with IoT-enabled healthcare, we propose a secure routing protocol that combines a fuzzy logic system and the Whale Optimization Algorithm (WOA) hierarchically. The suggested method consists of two primary approaches: the fuzzy trust strategy and the WOA-inspired clustering methodology. The first methodology plays a critical role in determining the trustworthiness of connected IoT equipment. Furthermore, a WOA-based clustering framework is implemented. A fitness function assesses the likelihood of IoT devices acting as cluster heads. This formula considers factors such as centrality, range of communication, hop count, remaining energy, and trustworthiness. Compared with other algorithms, the proposed method outperformed them in terms of network lifespan, energy usage, and packet delivery ratio by 47%, 58%, and 17.7%, respectively., (© 2024. The Author(s).)

This paper proposes a novel cache replacement technique based on the notion of combining periodic popularity prediction with size caching. The popularity, size, and time updates characteristics are used to calculate the value of each cache item. When it comes to content replacement, the information with the least value is first eliminated. Simulation results show that the proposed method outperforms the current algorithms in terms of cache hit rate and delay. The hit rate of the proposed scheme is 15.3% higher than GDS, 17.3% higher than MPC, 20.1% higher than LRU, 22.3% higher than FIFO, and 24.8% higher than LFU when 350 different categories of information are present. In real-world industrial applications such as including supply chain management, smart manufacturing, automation energy optimization, intelligent logistics transportation, and e-healthcare applications, it offers a foundation for the selection of caching algorithms., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Nalbant et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The provision of Wireless Fidelity (Wi-Fi) service in an indoor environment is a crucial task and the decay in signal strength issues arises especially in indoor environments. The Line-of-Sight (LOS) is a path for signal propagation that commonly impedes innumerable indoor objects damage signals and also causes signal fading. In addition, the Signal decay (signal penetration), signal reflection, and long transmission distance between transceivers are the key concerns. The signals lose their power due to the existence of obstacles (path of signals) and hence destroy received signal strength (RSS) between different communicating nodes and ultimately cause loss of the packet. Thus, to solve this issue, herein we propose an advanced model to maximize the LOS in communicating nodes using a modern indoor environment. Our proposal comprised various components for instance signal enhancers, repeaters, reflectors,. these components are connected. The signal attenuation and calculation model comprises of power algorithm and hence it can quickly and efficiently find the walls and corridors as obstacles in an indoor environment. We compared our proposed model with state of the art model using Received Signal Strength (RSS) and Packet Delivery Ratio (PDR) (different scenario) and found that our proposed model is efficient. Our proposed model achieved high network throughput as compared to the state-of-the-art models., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Waqas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Confidentiality requirements of individuals and companies led to the dominance of encrypted payloads in the overall Internet traffic. Hence, traffic classification on a network became increasingly difficult as it must rely on only the packet headers. Many vital tasks such as differential pricing, providing a safe Internet for children, and eliminating malicious connections require traffic classification, even if the payload contents are encrypted. Encrypted traffic is harder to classify as packet content becomes unreadable. In this work, we aim to provide an insight into traffic classification using encrypted packets in terms of both accuracy and packet processing time. LSTM (Long Short-Term Memory) architecture is a good candidate for this problem as it can handle sequences. Each flow can be modeled as a sequence and patterns of the sequences can provide valuable information. We compare the performance of LSTM with other methods in both real-time and offline experiments. Compared to a machine learning method both online and offline LSTM excelled with precision and recall differences up to 50%. Average accuracy with LSTM was measured as 97.77% offline and 91.7% in real-time. Average packet processing time in real-time was recorded as 0.593 msec which is 5 times faster than a recent work that uses LSTM method. [ABSTRACT FROM AUTHOR]

After the introduction of the first robotic-assisted surgical procedures, the technology soon reached the world of endovascular specialists, giving rise to several publications about robotic-assisted endovascular therapy. Compared with conventional procedures, roboticassisted procedures can be more accurate and reduce radiation exposure. The latest commercially available endovascular robotic system is the CorPath GRX, which can be operated remotely. Robotic-assisted approaches have proved applicable in the fields of coronary and peripheral vascular intervention and neurointervention. Remote intervention has already proved feasible in the coronary and peripheral vascular systems and, according to expert opinion, could revolutionize acute stroke management as well. We review current knowledge about robotic-assisted therapies and remote interventions, and the future prospects and pitfalls. [ABSTRACT FROM AUTHOR]

ObjectiveBuilding federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sustaining meaningful partnerships, and delivering clear and measurable system utility. We describe findings from two federated data sharing case examples that detail critical factors, shared outcomes, and production environment results.MethodsTwo federated data sharing pilot architectures developed to support network-based research associated with the University of Washington's Institute of Translational Health Sciences provided the basis for the findings. A spiral model for implementation and evaluation was used to structure iterations of development and support knowledge share between the two network development teams, which cross collaborated to support and manage common stages.ResultsWe found that using a spiral model of software development and multiple cycles of iteration was effective in achieving early network design goals. Both networks required time and resource intensive efforts to establish a trusted environment to create the data sharing architectures. Both networks were challenged by the need for adaptive use cases to define and test utility.ConclusionAn iterative cyclical model of development provided a process for developing trust with data partners and refining the design, and supported measureable success in the development of new federated data sharing architectures.

A recent innovation to help patients adhere to daily tuberculosis (TB) treatment over many months is video (or virtually) observed therapy (VOT). VOT is becoming increasingly feasible as mobile telephone applications and tablet computers become more widely available. Studies of the effectiveness of VOT in improving TB patient outcomes are being conducted.

A recent innovation to help patients adhere to daily tuberculosis (TB) treatment over many months is video (or virtually) observed therapy (VOT). VOT is becoming increasingly feasible as mobile telephone applications and tablet computers become more widely available. Studies of the effectiveness of VOT in improving TB patient outcomes are being conducted.

This article demonstrates an instrumented mouthguard capable of non-invasively monitoring salivary uric acid (SUA) levels. The enzyme (uricase)-modified screen printed electrode system has been integrated onto a mouthguard platform along with anatomically-miniaturized instrumentation electronics featuring a potentiostat, microcontroller, and a Bluetooth Low Energy (BLE) transceiver. Unlike RFID-based biosensing systems, which require large proximal power sources, the developed platform enables real-time wireless transmission of the sensed information to standard smartphones, laptops, and other consumer electronics for on-demand processing, diagnostics, or storage. The mouthguard biosensor system offers high sensitivity, selectivity, and stability towards uric acid detection in human saliva, covering the concentration ranges for both healthy people and hyperuricemia patients. The new wireless mouthguard biosensor system is able to monitor SUA level in real-time and continuous fashion, and can be readily expanded to an array of sensors for different analytes to enable an attractive wearable monitoring system for diverse health and fitness applications.

BackgroundCentralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner.ObjectiveThe objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies.Materials and methodsBased on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network.ResultsThe authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws.Discussion and conclusionFederated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks.

ObjectiveThe Cox proportional hazards model is a widely used method for analyzing survival data. To achieve sufficient statistical power in a survival analysis, it usually requires a large amount of data. Data sharing across institutions could be a potential workaround for providing this added power.Methods and materialsThe authors develop a web service for distributed Cox model learning (WebDISCO), which focuses on the proof-of-concept and algorithm development for federated survival analysis. The sensitive patient-level data can be processed locally and only the less-sensitive intermediate statistics are exchanged to build a global Cox model. Mathematical derivation shows that the proposed distributed algorithm is identical to the centralized Cox model.ResultsThe authors evaluated the proposed framework at the University of California, San Diego (UCSD), Emory, and Duke. The experimental results show that both distributed and centralized models result in near-identical model coefficients with differences in the range [Formula: see text] to [Formula: see text]. The results confirm the mathematical derivation and show that the implementation of the distributed model can achieve the same results as the centralized implementation.LimitationThe proposed method serves as a proof of concept, in which a publicly available dataset was used to evaluate the performance. The authors do not intend to suggest that this method can resolve policy and engineering issues related to the federated use of institutional data, but they should serve as evidence of the technical feasibility of the proposed approach.Conclusions WebDISCO (Web-based Distributed Cox Regression Model; https://webdisco.ucsd-dbmi.org:8443/cox/) provides a proof-of-concept web service that implements a distributed algorithm to conduct distributed survival analysis without sharing patient level data.

Objective: Analyze the possible relation between the documentary quality (DQ) versus the veracity of information (VI) on the syphilis and gonorrhea web pages. Methods: Descriptive cross-sectional study of websites containing information about syphilis and gonorrhea, by accessing this population through a Google. The quality was studied by using 8 variables belonging to DQ and 7 variables of the VI. Results: A total of 440 active websites mainly belonging to mass media and private entities was assessed. The fulfillment of DQ gave the following results: Mean 3.46 ± 0.07, median 4 and range from 0 to 7. The VI result was: median 4.07 ± 0.09, median 4 and range from 0 to 7. According to the search athwart syphilis or gonorrhea, the contrast of the median of the two indicators was: 3.55 vs 3.37 p = 0.181 and 4.14 vs 4.00 p = 0.442. No correlation was verified amid the data of DQ and VI (R = 0.04); p = 0.368. Similarly, no significance was observed when segregating data by disease, in the case of syphilis R = -0.03; p = 0.625 and on gonorrhea R = 0.12; p = 0.064. Conclusions: The DQ and VI bestowed low outcomes, which implies poor quality of syphilis and gonorrhea websites. According to infection (syphilis or gonorrhea), there were no meaningful differences amid the median values of the two indicators. Being acquainted with the authorship and affiliation of a website and the fact that it is tied to a prestigious web may be a factor to be deemed when predicting the VI of a website. The correlation amid the two indicators did not demonstrate an association, thus, knowing the DQ does not imply having the security of an adequate VI. [ABSTRACT FROM AUTHOR]

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Wireless communications have lately experienced substantial exploitation because they provide a lot of flexibility for data delivery. It provides connection and mobility by using air as a medium. Wireless sensor networks (WSN) are now the most popular wireless technologies. They need a communication infrastructure that is both energy and computationally efficient, which is made feasible by developing the best communication protocol algorithms. The internet of things (IoT) paradigm is anticipated to be heavily reliant on a networking architecture that is currently in development and dubbed software-defined WSN. Energy-efficient routing design is a key objective for WSNs. Cluster routing is one of the most commonly used routing techniques for extending network life. This research proposes a novel approach for increasing the energy effectiveness and longevity of software-defined WSNs. The major goal is to reduce the energy consumption of the cluster routing protocol using the firefly algorithm and high-efficiency entropy. According to the findings of the simulation, the suggested method outperforms existing algorithms in terms of system performance under various operating conditions. The number of alive nodes determined by the proposed algorithm is about 42.06% higher than Distributed Energy-Efficient Clustering with firefly algorithm (DEEC-FA) and 13.95% higher than Improved Firefly Clustering IFCEER and 12.05% higher than another referenced algorithm., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gökhan Nalbant et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Rotating Polarization Wave (RPW) is a novel Low Power Wide Area Networks (LPWAN) technology for robust connectivity and extended coverage area as compared to other LPWAN technologies such as LoRa and Sigfox when no error detection and correction is employed. Since, IoT and Machine-to-Machine (M2M) communication demand high reliability, RPW with error correction can significantly enhance the communication reliability for critical IoT and M2M applications. Therefore, this study investigates the performance of RPW with single bit error detection and correction using Hamming codes to avoid substantial overhead. Hamming (7,4) coded RPW shows a remarkable improvement of more than 40% in error performance compared to uncoded RPW thereby making it a suitable candidate for IoT and M2M applications. Error performance of coded RPW outperforms coded Chirp Spread Spectrum (CSS) modulation used in LoRa under multipath conditions by 51%, demonstrating superior adaptability and robustness under dynamic channel conditions. These findings provide valuable insights into the ongoing developments in wireless communication systems whilst reporting Q-RPW model as a new and effective method to address the needs of developing LPWAN and IoT ecosystems., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

With the escalation in the size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long Short-Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.

Leakage and tampering problems in collection and transmission of biomedical data have attracted much attention as these concerns instigates negative impression regarding privacy, security, and reputation of medical networks. This article presents a novel security model that establishes a threat-vector database based on the dynamic behaviours of smart healthcare systems. Then, an improved and privacy-preserved SRU network is designed that aims to alleviate fading gradient issue and enhance the learning process by reducing computational cost. Then, an intelligent federated learning algorithm is deployed to enable multiple healthcare networks to form a collaborative security model in a personalized manner without the loss of privacy. The proposed security method is both parallelizable and computationally effective since the dynamic behaviour aggregation strategy empowers the model to work collaboratively and reduce communication overhead by dynamically adjusting the number of participating clients. Additionally, the visualization of the decision process based on the explainability of features enhances the understanding of security experts by enabling them to comprehend the underlying data evidence and causal reasoning. Compared to existing methods, the proposed security method is capable of thoroughly analyzing and detecting severe security threats with high accuracy, reduce overhead and lower computation cost along with enhanced privacy of biomedical data.

Recent advances in aerial robotics and wireless transceivers have generated an enormous interest in networks constituted by multiple compact unmanned aerial vehicles (UAVs). UAV adhoc networks, i.e., aerial networks with dynamic topology and no centralized control, are found suitable for a unique set of applications, yet their operation is vulnerable to cyberattacks. In many applications, such as IoT networks or emergency failover networks, UAVs augment and provide support to the sensor nodes or mobile nodes in the ground network in data acquisition and also improve the overall network performance. In this situation, ensuring the security of the adhoc UAV network and the integrity of data is paramount to accomplishing network mission objectives. In this paper, we propose a novel approach to secure UAV adhoc networks, referred to as the blockchain-assisted security framework (BCSF). We demonstrate that the proposed system provides security without sacrificing the performance of the network through blockchain technology adopted to the priority of the message to be communicated over the adhoc UAV network. Theoretical analysis for computing average latency is performed based on queuing theory models followed by an evaluation of the proposed BCSF approach through simulations that establish the superior performance of the proposed methodology in terms of transaction delay, data secrecy, data recovery, and energy efficiency., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mohammed A. Alqarni. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The decoupling of control and forwarding layers brings Software-Defined Networking (SDN) the network programmability and global control capability, but it also poses SDN security risks. The adversaries can use the forwarding and control decoupling character of SDN to forge legitimate traffic, launching saturation attacks targeted at SDN switches. These attacks can cause the overflow of switch flow tables, thus making the switch cannot forward benign network traffic. How to effectively detect saturation attack is a research hotspot. There are only a few graph-based saturation attack detection methods. Meanwhile, the current graph generation methods may take useless or misleading information to the attack detection, thus decreasing the attack detection accuracy. To solve the above problems, this paper proposes TITAN, a bidirecTional forwardIng graph-based saturaTion Attack detectioN method. TITAN defines flow forwarding rules and topology information, and designs flow statistical features. Based on these definitions, TITAN generates nodes of the bi-forwarding graph based on the flow statistics features and edges of the bi-forwarding graph based on the network traffic routing paths. In this way, each traffic flow in the network is transformed into a bi-directional forwarding graph. Then TITAN feeds the above bidirectional forwarding graph into a Graph Convolutional Network (GCN) to detect whether the flow is a saturation attack flow. The experimental results show that TITAN can effectively detect saturation attacks in SDNs with a detection accuracy of more than 97%., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ran et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In wireless sensor networks, the implementation of clustering and routing protocols has been crucial in prolonging the network's operational duration by conserving energy. However, the challenge persists in efficiently optimizing energy usage to maximize the network's longevity. This paper presents CHHFO, a new protocol that combines a fuzzy logic system with the collaborative Harris Hawks optimization algorithm to enhance the lifetime of networks. The fuzzy logic system utilizes descriptors like remaining energy, distance from the base station, and the number of neighboring nodes to designate each cluster head and establish optimal clusters, thereby alleviating potential hot spots. Moreover, the Collaborative Harris Hawks Optimization algorithm employs an inventive coding mechanism to choose the optimal relay cluster head for data transmission. According to the results, the network throughput, HHOCFR is 8.76%, 11.73%, 8.64% higher than HHO-UCRA, IHHO-F, and EFCR. In addition, he energy consumption of HHOCFR is lower than HHO-UCRA, IHHO-F, and EFCR by 0.88%, 39.79%, 34.25%, respectively., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Advancements in biotechnology and molecular communication have enabled the utilization of nanomachines in Wireless Body Area Networks (WBAN2) for applications such as drug delivery, cancer detection, and emergency rescue services. To study these networks effectively, it is essential to develop an ideal propagation model that includes the channel response between each pair of in-range nanomachines and accounts for the interference received at each receiver node. In this paper, we employ an advection-diffusion equation to obtain a deterministic channel matrix through a vascular WBAN2. Additionally, the closed forms of inter-symbol interference (ISI) and co-channel interference (CCI) are derived for both full duplex (FDX) and half duplex transmission (HDX) modes. By applying these deterministic formulations, we then present the stochastic equivalents of the ideal channel model and interference to provide an innovative communication model by simultaneously incorporating CCI, ISI, and background noise. Finally, we evaluate the results with numerous experiments and use signal-to-interference-plus-noise ratio (SINR) and capacity as metrics.